U.S. Pat. No. 8,068,994 describes a method of performing impact analysis of genes (or proteins or metabolites or reactants) that are different between different phenotypes Without limitation, such phenotypes can be disease tissue vs. healthy tissue, treated with a drug vs. untreated, treated with drug A vs. treated with drug B, different time points in a time series measuring changes after a given intervention, etc. Without loss of generality, we will henceforth refer to the illustrative example of a phenotype comparison between disease and healthy.
The biological pathway(s) impacted in a disease state are predicted by (a) providing expression level data for a plurality of biomolecules differentially expressed in the disease state, compared with same biomolecules expressed in the healthy state; (b) determining the significance of the changes in the levels of the biomolecules in disease state; (c) determining the effect(s) of each biomolecule from the plurality of biomolecules on the expression of different downstream biomolecules within each pathway to provide a perturbation factor for each biomolecule in the pathway; (d) combining statistical significance of differentially expressed biomolecules present in the disease state, with a sum of perturbation factors for all of the biomolecules, generating an impact factor for each pathway; (e) calculating statistical significance of the observed impact factor based upon determined probability of having statistical significant presence of differentially expressed biomolecules in step (b) and the sum of perturbation factors in step (c); and (f) outputting statistical significance of the involvement of the pathway(s) in the given phenotype.
While the techniques disclosed in U.S. Pat. No. 8,068,994, work well, We have discovered that when conducting pathway analysis it is important to identify the interactions between different pathways that are significantly impacted in a given condition. None of the existing techniques are able to accomplish this. Currently available approaches calculate a p-value that aims to quantify the significance of the involvement of each pathway in the given phenotype. These p-values have traditionally been thought to be independent. We have discovered however, as the remainder of this disclosure will explain, that pathways can affect each other's p-values through a phenomena we call “crosstalk.” This disclosure also describes the first method able to: i) detect the presence of such crosstalk, ii) quantify its extent and iii) assess the impact on a pathway after removing this cross-talk effect.